GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images

Abstract

Molecular analysis has revealed extensive intra-tumor heterogeneity in human cancer samples, but cannot identify cell-to-cell variations within the tissue microenvironment. In contrast, in situ analysis can identify genetic aberrations in phenotypically defined cell subpopulations while preserving tissue-context specificity. GoIFISH is a widely applicable, user-friendly system tailored for the objective and semi-automated visualization, detection and quantification of genomic alterations and protein expression obtained from fluorescence in situ analysis. In a sample set of HER2-positive breast cancers GoIFISH is highly robust in visual analysis and its accuracy compares favorably to other leading image analysis methods. GoIFISH is freely available at www.sourceforge.net/projects/goifish/. Quantifying cell-to-cell heterogeneity in the tissue contextIntra-tumor heterogeneity is currently accepted as a hallmark of cancer, being present in virtually all tumor traits [1]. Sensitive molecular techniques developed in the last few years have allowed a detailed genetic and phenotypic deconvolution of intra-tumor heterogeneity. These include genome-wide analysis of bulk tumor samples to describe evolutionary trajectories in relapsed tumors and genomic divergence between primary tumors and metastases [2][3][4], as well as single-cell genomic profiling [2,5]. However, despite methodological improvements in the molecular characterization of single cells, the accurate interpretation of intra-tumor heterogeneity requires the inference of cell-to-cell variability within a particular tissue context, which can only be directly assessed by in situ analysis. Microenvironmental constraints within spatially restricted areas of a tumor can exert differential selective pressures, leading to the manifestation and the selection of different phenotypes and particular genotypes. For instance, different oxygen levels, the presence the development of objective analytical tools that minimize scoring subjectivity and facilitate the quantification of multiple traits in single cells while preserving context specificity. The implementation of these tools in both basic and translational research will advance our understanding of tumor biology and will facilitate biomarker discovery and validation.GoIFISH: quantifying tumor heterogeneity in IFISH images For application to IFISH, accurate segmentation at the nuclear, membrane and spot level are critical for subsequent analysis, which often interrogates clonal populations or evaluates relationships between protein and genomic expression. Objective integration of protein expression and copy number requires not only accurate segmentation, but also the separation of normal cells from tumor cells, and appropriate background subtraction associated with auto-fluorescence. Very few existing softwares allow manual alterations of small inaccuracies in cell segmentation and often incorrect cell classification results cannot be changed. Visual scoring by a trained observer (e.g. pathologist) is the gold...

Intratumor heterogeneity defines treatment‐resistant HER2+ breast tumors

“…The HER2 (CB11) primary antibody was detected with a secondary biotinylated antibody and visualized using streptavidin‐conjugated Alexa Fluor 488 antibody in order to visualize the protein expression of ER and HER2. A detailed IFISH protocol including antibody and BAC catalogue numbers is described in the previous publication (Trinh et al ., ). The tissue samples were mounted with DAPI counterstain, and areas of interest were photographed with 25 z‐stacks in a Zeiss Axiovision M1 microscope.…”

“…We previously developed and validated the software goifish (Trinh et al ., ), an image analysis pipeline designed to objectively recognize cell types, score protein intensities in distinct cellular compartments (nucleus, cytoplasm, and membranes), count and measure FISH spots/areas and intensities, measure nuclear size, and display topological distributions of the cells and the analyzed parameters. goifish estimates are highly concordant with visual scoring at the single‐cell level, and optimal intensity thresholds of 300 and 50 following adjustment by background and perinuclear intensity were used to define HER2‐positive and ER‐positive cells, respectively, from 12‐bit images (Trinh et al ., ). ER+ patients were identified according to the national guidelines with a cutoff level at 1% positive cells (Helsedirektoratet ).…”

“…For cluster analyses to study phenotypic heterogeneity, we assigned each cell within a tumor into one of four phenotypic groups (HER2+/ER+, HER2+/ER−, HER2−/ER+, HER2−/ER−) based on the defined thresholds. To address heterogeneity based on genomic changes, we assigned each cell into one of three HER2 CN categories as previously defined (Trinh et al ., ): normal ( HER2 norm), gain ( HER2 gain) or amplified ( HER2 amp). HER2 norm reflected cells with up to three spots (0–63 pixels), HER2 gain: three to six spots (64–200 pixels), and HER2 amp: > 6 spots (> 200 pixels).…”

“…To investigate and quantify the heterogeneity of HER2+ carcinomas by using single cell investigation, we performed detailed in situ analyses on samples from a Norwegian observational study (RA‐HER2), comprised of 37 HER2+ patients treated in a neoadjuvant setting with trastuzumab and chemotherapy where both response data and clinical follow‐up were available. For objective assessment of the molecular in situ markers, we used goifish , a software for image analysis developed to objectively score both immunofluorescence and FISH signals from numerous individual tumors cells (Trinh et al ., ). With this quantitative approach, we examined 103 images and more than 13 000 cells showing the clinical impact of different types of genomic and phenotypic intratumor heterogeneity in HER2+ breast cancer.…”

Personalized Cancer Care: Risk Prediction, Early Diagnosis, Progression, and Therapy

“…These analyses highlight the large repertoire of cancer genes and mutational processes operative in breast cancer. Specific alterations identified by such studies can further be analyzed using a combination of immunofluorescence and fluorescence in situ hybridization (FISH) techniques ("double immunoFISH") to identify intratumor heterogeneity in tumors from neoadjuvant-treated patients prior to and after therapy [24] . Results show that the genomic variability prior to therapy was more diverse in the partial responders than in the responders, and the remaining tumor was even more heterogeneous after treatment than prior to treatment.…”